SUMMARY

The continued growth of the world population and push for clean energy has created a political impetus to modernize the existing nuclear reactor fleet. Such efforts have culminated in the creation of the Generation IV International Forum, a multinational coalition formed with the purpose of researching and testing six advanced reactor prototypes. Of these designs, the molten salt reactor (MSR) has received notable attention due to its attractive characteristics of high thermodynamic efficiency, inherent safety features, and improved proliferation resistance. Despite these benefits, significant work remains to be done before MSRs can be deployed to the commercial grid. The variation in designs poses a significant challenge towards down selection and necessitates optimization methods that can reliably provide information about variables associated with different design metrics. This work addresses this challenge through a comprehensive modeling framework developed for both moderated and unmoderated liquid-fueled molten salt reactors. It does so through an extensive thermal hydraulic/neutronic coupled, time-dependent database of reactor designs that is dependent on multiple input variables associated with geometry, materials, and fuel salts. From this database, a predictive machine learning model is trained to determine output parameters given any set of input reactor design variables. This model is then used in a genetic algorithm optimization sequence. The optimization sequence allows for flexibility in the optimization process, where different metrics can be chosen for different design goals. For example, one case could involve multiplication factor constraints coupled with minimization of transuranics production, whereas another could focus on maximizing breeding ratio instead. The end result of the framework provides a potential core designer with the required input metrics for building a molten salt reactor based on user-defined performance metrics. https://bit.ly/3G52J5E